Biology subjects

Narykov, O.

Publications and source records attributed to Narykov, O..

7 recordsLinked to original sources

bioRxiv2025

A spatial long-read approach at near-single-cell resolution reveals developmental regulation of splicing and polyadenylation sites in distinct cortical layers and cell types.

Genome-wide single-cell and spatial long-read approaches have gained traction, but mostly lack single-cell resolution - and yield limited read lengths. Here, we introduce spatial ISOform sequencing (Spl-ISO-Seq), which reveals exons and polyadenylation sites from long reads with near-single-cell resolution. Spl-ISO-Seq selects long cDNAs and doubles to triples read lengths compared to standard preparations. Adding a highly specific software tool (Spl-ISOquant) and comparing human post-mortem pre-puberty samples of the visual cortex (8-11 years) to post-puberty samples (16-19 years), we find that cortical layers harbor stronger splicing and poly(A)-site regulation than the adjacent white matter, with enrichment of multiple protein-domain types. For oligodendrocytes however, developmental splicing changes are stronger in white matter. Among cortical layers, layer 4 has the most developmental changes in alternative-exon inclusion in excitatory neurons and in poly(A) sites. We also find many repeat elements, especially ERV1 long terminal repeats downstream of developmentally-regulated layer 4 exons. Overall, alternative splicing changes are linked to synapses - specifically at the post-synapse. Age-linked splicing changes in layers 1-3 and 4 are associated with autism spectrum disorder but not with schizophrenia, amyotrophic lateral sclerosis and Alzheimers disease. These results root developmental splicing changes during puberty and the resulting protein changes in specific layers and cell types. More generally, our new technologies enable new observations for any complex tissue.

genomics↗

bioRxiv2024

Data Imbalance in Drug Response Prediction - Multi-Objective Optimization Approach in Deep Learning Setting

Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning (ML) models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and increase the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy (MOORLE) loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to promote equitable outcomes in the healthcare field. Availabilityhttps://github.com/AlexandrNP/MOORLE Contactonarykov@anl.gov

cancer biology↗

bioRxiv2023

Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development

RNA isoforms influence cell identity and function. Until recently, technological limitations prevented a genome-wide appraisal of isoform influence on cell identity in various parts of the brain. Using enhanced long-read single-cell isoform sequencing, we comprehensively analyze RNA isoforms in multiple mouse brain regions, cell subtypes, and developmental timepoints from postnatal day 14 (P14) to adult (P56). For 75% of genes, full-length isoform expression varies along one or more axes of phenotypic origin, underscoring the pervasiveness of isoform regulation across multiple scales. As expected, splicing varies strongly between cell types. However, certain gene classes including neurotransmitter release and reuptake as well as synapse turnover, harbor significant variability in the same cell type across anatomical regions, suggesting differences in network activity may influence cell-type identity. Glial brain-region specificity in isoform expression includes strong poly(A)-site regulation, whereas neurons have stronger TSS regulation. Furthermore, developmental patterns of cell-type specific splicing are especially pronounced in the murine adolescent transition from P21 to P28. The same cell type traced across development shows more isoform variability than across adult anatomical regions, indicating a coordinated modulation of functional programs dictating neural development. As most cell-type specific exons in P56 mouse hippocampus behave similarly in newly generated data from human hippocampi, these principles may be extrapolated to human brain. However, human brains have evolved additional cell-type specificity in splicing, suggesting gain-of-function isoforms. Taken together, we present a detailed single-cell atlas of full-length brain isoform regulation across development and anatomical regions, providing a previously unappreciated degree of isoform variability across multiple scales of the brain.

genetics↗

bioRxiv2021

Molecular architecture of SARS-CoV-2 envelope by integrative modeling

Despite tremendous efforts by the research community during the COVID-19 pandemic, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. Being a key structural component of the SARS-CoV-2 virion, the envelope encapsulates viral RNA and is composed of three structural proteins, spike (S), membrane (M), and envelope (E), which interact with each other and with the lipids acquired from the host membranes. Here, we developed and applied an integrative multiscale computational approach to model the envelope structure of SARS-CoV-2 with near atomistic detail, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M protein. The molecular dynamics simulations allowed us to test the envelope stability under different configurations and revealed that the M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns formed by Ms transmembrane and intravirion (endo) domains. These results are in good agreement with current experimental data, demonstrating a generic and versatile integrative approach to model the structure of a virus de novo. We anticipate our work to provide insights into critical roles of structural proteins in the viral assembly and integration, proposing new targets for the antiviral therapies.

biophysics↗

bioRxiv2020

Structural genomics and interactomics of 2019 Wuhan novel coronavirus, 2019-nCoV, indicate evolutionary conserved functional regions of viral proteins

During its first month, the recently emerged 2019 Wuhan novel coronavirus (2019-nCoV) has already infected many thousands of people in mainland China and worldwide and took hundreds of lives. However, the swiftly spreading virus also caused an unprecedentedly rapid response from the research community facing the unknown health challenge of potentially enormous proportions. Unfortunately, the experimental research to understand the molecular mechanisms behind the viral infection and to design a vaccine or antivirals is costly and takes months to develop. To expedite the advancement of our knowledge we leverage the data about the related coronaviruses that is readily available in public databases, and integrate these data into a single computational pipeline. As a result, we provide a comprehensive structural genomics and interactomics road-maps of 2019-nCoV and use these information to infer the possible functional differences and similarities with the related SARS coronavirus. All data are made publicly available to the research community at http://korkinlab.org/wuhan

bioinformatics↗

bioRxiv2019

DISPOT: A simple knowledge-based protein domain interaction statistical potential

MotivationThe complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, finding which domains from each protein mediate the corresponding PPI is a challenging task.\n\nResultsHere, we present Domain Interaction Statistical POTential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their SCOP family annotations. The statistical potential is derived based on the analysis of more than 352,000 structurally resolved protein-protein interactions obtained from DOMMINO, a comprehensive database on structurally resolved macromolecular interactions\n\nAvailability and implementationDISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on Github: (https://github.com/KorkinLab/DISPOT) and standalone docker images on DockerHub: (https://cloud.docker.com/u/korkinlab/repository/docker/korkinlab/dispot).

bioinformatics↗

bioRxiv2018

Determining rewiring effects of alternatively spliced isoforms on protein-protein interactions using a computational approach

The critical role of alternative splicing (AS) in cell functioning has recently become apparent, whether in studying tissue-or cell-specific regulation, or understanding molecular mechanisms governing a complex disorder. Studying the rewiring, or edgetic, effects of alternatively spliced isoforms on protein interactome can provide system-wide insights into these questions. Unfortunately, high-throughput experiments for such studies are expensive and time-consuming, hence the need to develop an in-silico approach. Here, we formulated the problem of characterization the edgetic effects of AS on protein-protein interactions (PPIs) as a binary classification problem and introduced a first computational approach to solve it. We first developed a supervised feature-based classifier that benefited from the traditional features describing a PPI, the problem-specific features that characterized the difference between the reference and alternative isoforms, and a novel domain interaction potential that allowed pinpointing the domains employed during a specific PPI. We then expanded this approach by including a large set of unlabeled interactomics data and developing a semi-supervised learning method. Our method called AS-IN (Alternatively Splicing INteraction prediction) Tool was compared with the state-of-the-art PPI prediction tools and showed a superior performance, achieving 0.92 in precision and recall. We demonstrated the utility of AS-IN Tool by applying it to the transcriptomic data obtained from the brain and liver tissues of a healthy mouse and western diet fed mouse that developed type two diabetes. We showed that the edgetic effects of differentially expressed transcripts associated with the disease condition are system-wide and unlikely to be detected by looking only at the gene-specific expression levels.

bioinformatics↗